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9 ‘Become a Data Engineer’ Courses by LinkedIn Learning – Perfect for Beginners

9 'Become a Data Engineer' Courses by LinkedIn Learning - Perfect for Beginners
Data Science

9 ‘Become a Data Engineer’ Courses by LinkedIn Learning – Perfect for Beginners

LinkedIn Learning has compiled 9 ‘Become a Data Engineer’ Courses with a free trial month included to help students learn Data Engineering while saving a buck.

9 'Become a Data Engineer' Courses by LinkedIn Learning - Perfect for Beginners

Build extensive data engineering and DevOps skills as you learn essential concepts. With this learning path, master the tools of the trade and how to apply them in real-world data project environments and platforms.

The ‘Become a Data Engineer‘ learning path includes 9 different courses and provides a certificate of completion upon each course completion.

1. Data Science Foundations: Data Engineering – Click here to Enroll

Approach big data with confidence by mastering the core skills needed to put data to work for your business. This course covers the basics of data engineering, system design, analytics, and business intelligence.

Data Science Foundations: Data Engineering - 9 'Become a Data Engineer' Courses by LinkedIn Learning

Data science expert Ben Sullins explains how to collect and organize your data so you can deliver results that your organization can leverage. Ben starts by examining the modern data ecosystem and how it relates to running a smart and efficient data hub.

Then, he shows you how to perform the principle tasks involved in managing, loading, extracting, and transforming data. He also takes you through staging, profiling, cleansing, and migrating data. Along the way, he provides actionable recommendations that applicable to data experts throughout an organization—analysts, engineers, scientists, modelers, and more.

2. NoSQL Essential Training – Click here to Enroll

As the shiny new object in the data world, you might have heard a lot of people talk excitedly about NoSQL and all the things it can do. It’s great in terms of flexibility, speed, and is easy to work with. It’s super scalable, so it can accommodate increased numbers of users as websites and applications grow. But will it replace SQL? Will it make relational databases obsolete?

NoSQL Essential Training - 9 'Become a Data Engineer' Courses by LinkedIn Learning

In this course, Mel McGee explains just exactly what NoSQL is, the pros and cons, and tradeoffs you’ll make when using NoSQL. Mel takes a high-level approach without delving into the details of any one NoSQL query language or solution, so if you’re a developer looking for a bigger picture of NoSQL, or an entrepreneur wanting to explore options for your product, or just plain curious about non-relational databases, this course is for you.

3. Apache Spark Essential Training: Big Data Engineering – Click here to Enroll

Data engineering is the foundation for building analytics and data science applications in the new Big Data world. Data engineering requires combining multiple big data technologies to construct data pipelines and networks to stream, process, and store data.

Apache Spark Essential Training: Big Data Engineering - 9 'Become a Data Engineer' Courses by LinkedIn Learning

This course focuses on building full-fledged solutions that combine Apache Spark with other Big Data tools to create end-to-end data pipelines. Instructor Kumaran Ponnambalam begins by defining data engineering, its functions, and its concepts. Next, Kumaran goes over how Spark capabilities such as parallel processing, execution plans, state management options, and machine learning work with extract, transform, load (ETL). He introduces you to batch processing use cases and processes, as well as real-time processing pipelines. After walking you through several useful best practices, Kumaran concludes with an end-to-end exercise project.

4. Architecting Big Data Applications: Batch Mode Application Engineering – Click here to Enroll

Batch mode consolidates data-related operations in order to reduce the load on networks. Batch mode helps software architects build big data applications that operate smoothly and efficiently under real-world conditions. In this course, you can learn about use cases and best practices for architecting batch mode applications using technologies such as Hive and Apache Spark.

Architecting Big Data Applications: Batch Mode Application Engineering - 9 'Become a Data Engineer' Courses by LinkedIn Learning

There is no coding involved. Instead you will see how big data tools can help solve some of the most complex challenges for businesses that generate, store, and analyze large amounts of data. The use cases are drawn from a variety of industries, including ecommerce and IT. Instructor Kumaran Ponnambalam shows how to analyze a problem, draw an architectural outline, choose the right technologies, and finalize the solution. After each use case, he reviews related best practices for data acquisition, transport, processing, storage, and service. Each lesson is rich in practical techniques and insights from a developer who has experienced the benefits and shortcomings of these technologies firsthan

5. Architecting Big Data Applications: Real-Time Application Engineering – Click here to Enroll

Real-time systems have guaranteed response times that can be sub-seconds from the trigger. Meaning that when a user clicks a button, your app better respond—and fast. Architecting applications under real-time constraints is an even bigger challenge when you’re dealing with big data. Excessive latency can cost you money, in terms of system resources consumed and customers lost. Luckily, big data technology and efficient architecture can provide the real-time responsiveness your business needs. In this course, you can learn about use cases and best practices for architecting real-time applications with technologies such as Kafka, Hazelcast, and Apache Spark.

Architecting Big Data Applications: Real-Time Application Engineering - 9 'Become a Data Engineer' Courses by LinkedIn Learning

There is no coding involved. Instead you will see how big data tools can help solve some of the most complex challenges for businesses that generate, store, and analyze large amounts of data. The use cases are drawn from a variety of industries, including ecommerce and IT. Instructor Kumaran Ponnambalam shows how to analyze a problem, draw an architectural outline, choose the right technologies, and finalize the solution. After each use case, he reviews related best practices for real-time streaming, predictive analytics, parallel processing, and pipeline management. Each lesson is rich in practical techniques and insights from a developer who has experienced the benefits and shortcomings of these technologies firsthand.

6. SQL: Data Reporting and Analysis – Click here to Enroll

Do you rely on IT to get the data you need? Are you often stuck waiting in line for data, and wish you could just retrieve it yourself? In this course, learn how to get the data you want by writing a bit of SQL code. You won’t just be able to pull data out of the database; you’ll be able to manipulate it: merging it, grouping it, and relabeling it to get just the report you want.

SQL: Data Reporting and Analysis - 9 'Become a Data Engineer' Courses by LinkedIn Learning

Join Emma Saunders as she shows how to write simple SQL queries for data reporting and analysis using a publicly accessible online database. Learn how to filter, group, and sort data, using built-in SQL functions to format or calculate results. Discover how to perform more complex queries, such as joining data together from different database tables. Last but not least, she introduces views, procedures, functions, and variables.

7. Advanced NoSQL for Data Science – Click here to Enroll

Many organizations are turning to NoSQL databases to store large volumes of complex data, sparking an increased need for data scientists and analysts to understand non-relational data stores. If you’re a data scientist or business analyst who needs to work with NoSQL, then this course is for you. Learn about the differences between relational and NoSQL databases, review types of NoSQL databases, and see how to perform common data science tasks, including data preparation, exploration, and building and applying models.

Advanced NoSQL for Data Science - 9 'Become a Data Engineer' Courses by LinkedIn Learning

The course begins with an introduction to NoSQL, and then delves into the specifics of document, wide-column, and graph databases. Learn key details for performing data preparation, exploration, and extraction for each type of NoSQL database. Review case studies that show how to use various NoSQL databases with popular data science tools, including the document database MongoDB, the wide-column database Cassandra, and the graph database Neo4j.

8. SQL Tips, Tricks, & Techniques – Click here to Enroll

Get Ben Sullins’s 12 must-have SQL techniques for data science pros—engineers, DevOps, data miners, programmers, and other systems specialists. Ben’s tips focus on practical applications of SQL queries for data analysis.

SQL Tips, Tricks, & Techniques - 9 'Become a Data Engineer' Courses by LinkedIn Learning

Learn how to retrieve data, join tables, calculate rolling averages and rankings, work with dates and times, use window functions, aggregate and filter data, and much more. Each tip is short, relevant, and up to date with current industry best practices—making this the perfect course for busy analysts who normally struggle to find time to build their skills.

9. Cloud NoSQL for SQL Professionals – Click here to Enroll

NoSQL databases can store nonrelational data on a super large scale and solve problems regular databases can’t handle: indexing the entire internet, predicting subscriber behavior, or targeting ads on a platform as large as Facebook. But with over 150 NoSQL database types, it can be hard for a SQL professional to know where to start.

Cloud NoSQL for SQL Professionals - 9 'Become a Data Engineer' Courses by LinkedIn Learning

In this course, Lynn Langit breaks down these types into a few main categories and shows how to get your own NoSQL database up and running with easy-to-configure cloud solutions. Learn how to add and query data, apply the CAP theorem with NoSQL, and leverage key NoSQL trends such as multifunctionality and data lake NoSQL alternatives. Plus, explore AWS and GCP NoSQL database services such as DynamoDB, ElastiCache, and Bigtable.

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